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| Course Description |
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| Course Name |
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Order Statistics and Inference |
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| Course Code |
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ISB-566 |
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| Course Type |
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Optional |
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| Level of Course |
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Second Cycle |
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| Year of Study |
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1 |
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| Course Semester |
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Spring (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
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Assoc.Prof.Dr. ALİ İHSANGENÇ
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| Learning Outcomes of the Course |
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Know the theory of the order statistics.
Know the estimators based on order statistics.
Know how to compute the estimates using a statistical software.
Apply the comparison criteria among various estimates.
Know estimation methods in regression models.
Know estimation methods in a censored sample case.
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| Mode of Delivery |
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Face-to-Face |
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| Prerequisites and Co-Prerequisites |
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None |
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| Recommended Optional Programme Components |
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None |
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| Aim(s) of Course |
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This course aims to exploit order statistics extensively in statistical inference especially in estimation for both complete samples and censored samples. |
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| Course Contents |
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Distribution of order statistics, moments of order statistics and moment relations, results for some well known distributions, linear estimation, BLUE, maximum likelihood estimation, approximate maximum likelihood estimation, optimal estimation based on selected order statistics, Cohen-Whitten estimators, estimation with regression models, censored samples |
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| Language of Instruction |
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Turkish |
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| Work Place |
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Seminary room in Department of Statisitics |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
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1 |
Order statistics theory |
Reading the references |
Lecture, discussion and problem solving |
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2 |
Order statistics theory |
Reading the references |
Lecture, discussion and problem solving |
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3 |
Moments of order statistics, moment relations |
Reading the references |
Lecture, discussion and problem solving |
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4 |
Linear estimation |
Reading the references |
Lecture, discussion and problem solving |
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5 |
Linear estimation |
Reading the references |
Lecture, discussion and problem solving |
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6 |
Maximum likelihood estimation |
Reading the references |
Lecture, discussion and problem solving |
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7 |
Approximate maximum likelihood estimation |
Reading the references |
Lecture, discussion and problem solving |
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8 |
Midterm exam |
Review the topics discussed in the lecture notes and sources |
Written exam |
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9 |
Optimal estimation based on selected order statistics |
Reading the references |
Lecture, discussion and problem solving |
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10 |
Cohen-Whitten estimators |
Reading the references |
Lecture, discussion and problem solving |
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11 |
Cohen-Whitten estimators |
Reading the references |
Lecture, discussion and problem solving |
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12 |
Estimation for regression models |
Reading the references |
Lecture, discussion and problem solving |
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13 |
Estimation for regression models |
Reading the references |
Lecture, discussion and problem solving |
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14 |
Censored samples |
Reading the references |
Lecture, discussion and problem solving |
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15 |
Censored samples |
Reading the references |
Lecture, discussion and problem solving |
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16/17 |
Final exam |
Review the topics discussed in the lecture notes and sources |
Written exam |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 N. Balakrishnan, A. C. Cohen, Order Statistics and Inference, Academic Press, 1991.
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| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
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Mid-term Exams (Written, Oral, etc.) |
1 |
60 |
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Homeworks/Projects/Others |
6 |
40 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
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Final Assessments
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100 |
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Rate of Final Assessments to Success
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60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
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1 |
Possess advanced level of theoretical and applicable knowledge in the field of Probability and Statistics. |
5 |
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2 |
Conduct scientific research on Mathematics, Probability and Statistics. |
5 |
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3 |
Possess information, skills and competencies necessary to pursue a PhD degree in the field of Statistics. |
0 |
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4 |
Possess comprehensive information on the analysis and modeling methods used in Statistics. |
0 |
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5 |
Present the methods used in analysis and modeling in the field of Statistics. |
0 |
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6 |
Discuss the problems in the field of Statistics. |
0 |
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7 |
Implement innovative methods for resolving problems in the field of Statistics. |
0 |
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8 |
Develop analytical modeling and experimental research designs to implement solutions. |
0 |
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9 |
Gather data in order to complete a research. |
0 |
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10 |
Develop approaches for solving complex problems by taking responsibility. |
0 |
|
11 |
Take responsibility with self-confidence. |
0 |
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12 |
Have the awareness of new and emerging applications in the profession |
0 |
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13 |
Present the results of their studies at national and international environments clearly in oral or written form. |
0 |
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14 |
Oversee the scientific and ethical values during data collection, analysis, interpretation and announcment of the findings. |
0 |
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15 |
Update his/her knowledge and skills in statistics and related fields continously |
0 |
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16 |
Communicate effectively in oral and written form both in Turkish and English. |
0 |
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17 |
Use hardware and software required for statistical applications. |
0 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
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Class Time (Exam weeks are excluded) |
14 |
3 |
42 |
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Out of Class Study (Preliminary Work, Practice) |
14 |
3 |
42 |
| Assesment Related Works |
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Homeworks, Projects, Others |
6 |
7 |
42 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
18 |
18 |
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Total Workload: | 154 |
| Total Workload / 25 (h): | 6.16 |
| ECTS Credit: | 6 |
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